👾Jsonify’s Post

CRUD support is coming to RAW! Create, update, and delete data in real-time from your APIs, enabling more dynamic interactions for AI agents, apps and LLMs! Learn more at https://zurl.co/TPdU

I wanted to give you a heads-up on an exciting new tutorial I’ve got in the works. This video is all about building a full-stack SaaS application that pulls in YouTube comments, processes them with CrewAI enterprise, and generates actionable content ideas. If you’re interested in building full-stack AI applications, this is for you! 🛠️ Here’s What You’ll Learn In this tutorial, I’ll guide you through each part of the process of creating this full-stack application. Here’s a breakdown: 1️⃣ Building the Frontend and Backend: We’ll use Next.js to set up the app and deploy it on Vercel. Plus, you’ll get hands-on experience connecting to a Neon Postgres database to manage data. 2️⃣ Integrating with CrewAI Enterprise: Learn how to harness CrewAI’s enterprise features to analyze YouTube comments, filter out casual messages, and focus on meaningful feedback. You’ll see how to create a system that automates data analysis and transforms raw comments into structured, actionable insights. 3️⃣ Generating Video Titles and Descriptions: We’ll configure CrewAI to turn filtered comments into potential video titles and descriptions. You’ll build a workflow that streamlines idea generation and content planning, using CrewAI’s advanced capabilities. 💡 Why This Tutorial is Worth Your Time This tutorial doesn’t just cover building one app—it teaches you how to apply the synthesize pattern for data processing, a core skill in AI development. Here’s why this matters: ✅ Real-World Adaptability: The synthesize pattern goes beyond YouTube. After learning it, you can apply it to dozens of other applications where large datasets need to be turned into insights. Imagine using this pattern for customer feedback, product analysis, or trend monitoring—there are endless opportunities to build your own AI-powered apps! ✅ Hands-On Full-Stack Skills: Get practical experience with tools like Next.js, Vercel, and Neon, and learn how to bring everything together into a seamless app. 📅 How to Catch the Release I’ll be releasing this video on my YouTube channel. Head over there, subscribe, and turn on notifications to be the first to know when it drops! I’ll also have links to my YouTube, my free Skool community, and the Pro community down in the comments below. Drop any questions you have, and see you soon! 👋 CrewAI Neon Vercel

I've been loving building with Apollo GraphQL Connectors! It's amazing how quickly I can join so many things together with a delightful DX. I wanted to share more of that with everyone. I showed a connector for Strapi v5 in my demo, so I built out a full "users" connector that you can use today after following our quickstart docs: https://lnkd.in/gjiQdqW9 How is this maybe different then the GraphQL API Strapi ships with? 1. It's actually not that different, the Strapi GraphQL API just calls the internal REST API. That's what Connectors is doing. 2. The Strapi GraphQL API is a pre-defined shape that you can only influence so much. Connectors lets you express the API anyway you want! 3. This connector is just a portion of the API and works with any Strapi v5 instance. No pre-defined patterns for Strapi Content-Types you create that can constrain you. You're free to express your Content-Types in whatever way you want. You can use the Apollo VS Code extension to do this quickly and use my gist as a reference pattern to build/join whatever you want. 4. You're all setup to start joining other REST APIs or other GraphQL APIs that are all unified in a single endpoint. You just can't do that with the current Strapi GraphQL API because...well it just wasn't built for that. Strapi was built to be an amazing Headless CMS, which it is and my personal favorite ❤️ There's so much more you can do with Apollo Connectors. I clipped out the full demo from our keynote so you can watch just that portion where I work with Strapi and Stripe(~9 min); a follow up to my previous post. Don't worry, I got a Stripe connector coming for ya'll 🔜 If you want to see the full version of the keynote and my AI chatbot built on top of that graph, we're doing a virtual GraphQL Summit that gives you that and so much more great content. Check it out and register for free 👉 https://lnkd.in/g7AGph4f

Want to streamline your application development process and enhance user experience? Enjoy consistent data formats with outputs following a strict JSON Schema, eliminating manual tweaking. Benefit from flexible tool integrations with support for specific function signatures, ensuring precise outputs. Plus, simplify data payload creation for improved user experience. #DataFormats #JSONSchema #ApplicationDevelopment #UserExperience

[1/2] Last week, I attended C3.ai's Transform conference at The Boca Raton in Florida. This two-day event presented what I believe is the most compelling demonstration of **actually useful** generative AI for enterprise and industry that I've seen yet in 2024. The conference kicked off with CEO Tom Siebel's morning speech, setting the tone for 43 intense hours of product demos, actual lectures, panel discussions, customer interviews and in-depth dev team conversations. Contrary to my initial impressions that the vast, opulent venue and hard-hitting speaker list might be papering over the nascent state of LLM-driven and LLM-augmented enterprise software, the event instead effectively demonstrated that GenAI is evolving beyond the X-Twitter/Reddit/hustlebro hype and beginning to deliver productivity to things that actually matter. C3’s vision for the future is enterprise search. Yep – the familiar search box, introduced by AltaVista in 1995, popularised by Yahoo and now known as “Googling”. But C3 isn’t trying to compete with Google or Bing or even Perplexity. Even Google is beginning to drown in SEO-optimised mush flooding the open web. What C3 offer instead is an internal search engine to replace the clunky, keyword-dependent and domain-naïve systems built into Windows Explorer, Finder, Outlook and SharePoint. The AI community will say, “but don’t semantic search engines already exist? FAISS has been around forever. Typesense just added vector search. Open-source embedding models are effective, available and lightweight enough to run fast on my Dell laptop. Hugging Face has everything. I have this great Streamlit frontend so my whole team can make use of this Python backend! Even NVIDIA released a great consumer tool to chat with local docs!” Those in industry will know the challenges of scaling enterprise and industrial data search – and DataOps – beyond individual users or small, digital native teams is far harder than pooling, extracting, chunking and indexing. Real-world IT and OT systems won’t have an API with great web documentation. Some might have been installed by a system integrator in 2003 that has since changed names twice. Some are proprietary. Some might even need a $1200 cable and adapter to physically connect to them to your network. C3 AI – and its closest competitors Cognite and Palantir – have spent years building and refining many such data connectors and offer them, in most cases, as turnkey solutions. [continued in 2/2] #C3Transform

The interview provides good insights on how GoDaddy, a leading web hosting platform, went from zero to 50 LLMs for clients and employees, its culture of experimentation, and data readiness. "different LLMs are leapfrogging each other in cost, accuracy, reliability and security." "We want to have a measurable hypothesis or what it is that generative AI will deliver to those...innovation without some kind of hypothesis and some kind of measurement is novelty"  "We’ve built a common gateway that talks to all the various LLMs on the backend.....that gateway is responsible both for implementing the guardrails…, and evaluate the responses back from the LLMs to determining if we’re seeing pattern that we need to be aware of showing it’s not working as intended." https://lnkd.in/gBNF9Gc5

✅ extremely fast ⚡️ ✅ massively scalable 📈 ✅ $1,000 credits to get started 👍 ✅ cost-effective 💰 ✅ hybrid search 🔍 If you are planning to build production-ready #RAG #GenAI #Search #Recsys applications, check out #VertexAI Vector Search Real-world benchmarks, customer use cases, and other details in the latest blog post 👇 https://lnkd.in/eKAZrk6t

I’ve stumbled across Bee Agent Framework, and it’s been an impressive experience. If you’re exploring agentic AI workflows, this framework deserves your attention. What struck me the most is how intuitive and modular it is. Perfect for building intelligent agents that can handle complex tasks, and its almost production ready. Workflows felt natural, and has a clean architecture. Best of all it’s open-source, so there’s plenty of room to explore, adapt, and contribute to its community. Its OpenAI compatible or BYO(Model). Highly recommend giving it a try. npm install bee-agent-framework to get started Would love to hear your thoughts if you’ve used it! Let’s compare notes. 🐝 https://lnkd.in/eTQH7Zir #AI #AgentFramework #BeeAgentFramework #AIWorkflows #OpenSource #SmartAgents #AgenticAI

Here's how you can save 1000s of dollars in building AI copilots: It can take from weeks to months to build copilots from scratch. CopilotKit simplifies this. CopilotKit on GitHub: https://lnkd.in/dkKkM_rT. It is a low-code open-source toolkit primarily dedicated to building AI copilots. It provides all functionalities that facilitate AI integration like: - Collecting data from the app to send to the model. - Managing any third-party interactions an app may involve. - Performing actions to display the response received from the model. - Making updates to the application’s backend/frontend from the model response. Recently, CopilotKit v1.0 was released with many upgrades. Here are a few key updates that will make it much easier to build copilots. 1) Generative UI - AI copilots typically demand some visual engagement. - For instance, when a user interacts, it should dynamically generate visual components. This includes charts, interactive widgets, etc., that are specific to the user's context. - CopilotKit v1.0 comes with these capabilities, and a demo is shown below: 2) Rebuilt with GraphQL - Earlier versions of CopilotKit utilized simple LLM REST API calls. In the latest release, the communication protocol has been redone with GraphQL. - With GraphQL, the copilot engine can handle typed and dedicated input fields. Moreover, it also helps in returning various copilot-specific typed and dedicated output fields. 3) Improved React Hooks CopilotKit has various react hooks for providing app-state-related information to the Copilot engine. These details can then be sent to the AI model. There are a few updates here: - useCopilotAction hook to invoke actions by AI based on the context of a chat. - useCopilotChatSuggestions hook dynamically generates chat suggestions based on the app state. Other than these three cool upgrades, CopilotKit v1.0 also comes with a managed Copilot Cloud (currently in beta). I summarized these updates in more detail in yesterday's issue of Daily Dose of Data Science. The newsletter is linked in the comments. Thanks to CopilotKit for collaborating with me today. 👉 Over to you: What are some other challenges of building AI copilots?

Hexofy Review: The AI-Powered Web Scraping Tool Hexofy is a cutting-edge browser extension designed to make web scraping and data extraction effortless. It promises to transform the way users gather data from the web, offering a blend of simplicity and powerful features, thanks to its integration with artificial intelligence. In this comprehensive review, we'll delve into the features, usability, pros, and cons of Hexofy, and see how it stands out in the crowded space of web scraping tools. #AI #WebScraping #DataExtraction #ProductivityTools #DigitalMarketing #LeadGeneration #Automation #ArtificialIntelligence #Hexofy #TechReview #DataProcessing #GoogleSheets #BrowserExtension #TechTools https://lnkd.in/gj8Cd_8e